With the introduction of deep neural networks to applications in machine translation, more fluent outputs have been achieved with neural machine translation (NMT) than with statistical machine translation [1]}.
However, a fluent NMT output requires a large parallel corpus, which is difficult to prepare.
Therefore, several studies have attempted to improve fluency in NMT without the use of a large parallel corpus.
